A Study on the Integration of Pipeline and E2E SLU systems for Spoken Semantic Parsing toward STOP Quality Challenge
Recently there have been efforts to introduce new benchmark tasks for spoken language understanding (SLU), like semantic parsing. In this paper, we describe our proposed spoken semantic parsing system for the quality track (Track 1) in Spoken Language Understanding Grand Challenge which is part of I...
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| creator | Arora, Siddhant Futami, Hayato Wu, Shih-Lun Huynh, Jessica Peng, Yifan Kashiwagi, Yosuke Tsunoo, Emiru Yan, Brian Watanabe, Shinji |
| description | Recently there have been efforts to introduce new benchmark tasks for spoken
language understanding (SLU), like semantic parsing. In this paper, we describe
our proposed spoken semantic parsing system for the quality track (Track 1) in
Spoken Language Understanding Grand Challenge which is part of ICASSP Signal
Processing Grand Challenge 2023. We experiment with both end-to-end and
pipeline systems for this task. Strong automatic speech recognition (ASR)
models like Whisper and pretrained Language models (LM) like BART are utilized
inside our SLU framework to boost performance. We also investigate the output
level combination of various models to get an exact match accuracy of 80.8,
which won the 1st place at the challenge. |
| doi_str_mv | 10.48550/arxiv.2305.01620 |
| format | Article |
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language understanding (SLU), like semantic parsing. In this paper, we describe
our proposed spoken semantic parsing system for the quality track (Track 1) in
Spoken Language Understanding Grand Challenge which is part of ICASSP Signal
Processing Grand Challenge 2023. We experiment with both end-to-end and
pipeline systems for this task. Strong automatic speech recognition (ASR)
models like Whisper and pretrained Language models (LM) like BART are utilized
inside our SLU framework to boost performance. We also investigate the output
level combination of various models to get an exact match accuracy of 80.8,
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language understanding (SLU), like semantic parsing. In this paper, we describe
our proposed spoken semantic parsing system for the quality track (Track 1) in
Spoken Language Understanding Grand Challenge which is part of ICASSP Signal
Processing Grand Challenge 2023. We experiment with both end-to-end and
pipeline systems for this task. Strong automatic speech recognition (ASR)
models like Whisper and pretrained Language models (LM) like BART are utilized
inside our SLU framework to boost performance. We also investigate the output
level combination of various models to get an exact match accuracy of 80.8,
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language understanding (SLU), like semantic parsing. In this paper, we describe
our proposed spoken semantic parsing system for the quality track (Track 1) in
Spoken Language Understanding Grand Challenge which is part of ICASSP Signal
Processing Grand Challenge 2023. We experiment with both end-to-end and
pipeline systems for this task. Strong automatic speech recognition (ASR)
models like Whisper and pretrained Language models (LM) like BART are utilized
inside our SLU framework to boost performance. We also investigate the output
level combination of various models to get an exact match accuracy of 80.8,
which won the 1st place at the challenge.</abstract><doi>10.48550/arxiv.2305.01620</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computation and Language Computer Science - Sound |
| title | A Study on the Integration of Pipeline and E2E SLU systems for Spoken Semantic Parsing toward STOP Quality Challenge |
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